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Bioinformatics Advance Access originally published online on October 25, 2005
Bioinformatics 2005 21(24):4384-4393; doi:10.1093/bioinformatics/bti732
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Haplotype-based linkage disequilibrium mapping via direct data mining

Jing Li 1,* and Tao Jiang 2,3,4

1Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH 44106, USA
2Department of Computer Science and Engineering, University of California Riverside, CA 92521, USA
3Center for Advanced Study, Tsinghua University Beijing, China
4Shanghai Center for Bioinformatics Technology Shanghai, China

*To whom correspondence should be addressed.

Motivation: With the availability of large-scale, high-density single-nucleotide polymorphism markers and information on haplotype structures and frequencies, a great challenge is how to take advantage of haplotype information in the association mapping of complex diseases in case–control studies.

Results: We present a novel approach for association mapping based on directly mining haplotypes (i.e. phased genotype pairs) produced from case–control data or case–parent data via a density-based clustering algorithm, which can be applied to whole-genome screens as well as candidate-gene studies in small genomic regions. The method directly explores the sharing of haplotype segments in affected individuals that are rarely present in normal individuals. The measure of sharing between two haplotypes is defined by a new similarity metric that combines the length of the shared segments and the number of common alleles around any marker position of the haplotypes, which is robust against recent mutations/genotype errors and recombination events. The effectiveness of the approach is demonstrated by using both simulated datasets and real datasets. The results show that the algorithm is accurate for different population models and for different disease models, even for genes with small effects, and it outperforms some recently developed methods.

Availability: The software, HapMiner, and Supplementary materials are available on the authors' website at http://vorlon.case.edu/~jxl175/HapMiner.html

Contact: jingli{at}eecs.case.edu

Received on August 17, 2005; revised on October 4, 2005; accepted on October 19, 2005

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